Overview

Dataset statistics

Number of variables21
Number of observations21611
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory200.5 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

price is highly overall correlated with sqft_living and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with price and 6 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 6 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
sqft_basement has 13125 (60.7%) zerosZeros
yr_renovated has 20697 (95.8%) zerosZeros

Reproduction

Analysis started2023-01-31 00:57:17.130065
Analysis finished2023-01-31 00:58:34.003740
Duration1 minute and 16.87 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct21434
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5803274 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:34.228731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1235031 × 108
Q12.1230493 × 109
median3.9049304 × 109
Q37.3089005 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858512 × 109

Descriptive statistics

Standard deviation2.8765917 × 109
Coefficient of variation (CV)0.62803189
Kurtosis-1.2605153
Mean4.5803274 × 109
Median Absolute Deviation (MAD)2.4025301 × 109
Skewness0.243339
Sum9.8985455 × 1013
Variance8.2747796 × 1018
MonotonicityNot monotonic
2023-01-30T21:58:34.467646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
7983000200 2
 
< 0.1%
3293700496 2
 
< 0.1%
8945100320 2
 
< 0.1%
7520000695 2
 
< 0.1%
3904100089 2
 
< 0.1%
6308000010 2
 
< 0.1%
7888000390 2
 
< 0.1%
7200179 2
 
< 0.1%
5332200530 2
 
< 0.1%
Other values (21424) 21590
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2023-01-30T21:58:34.718705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:34.958720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540085.03
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:35.198711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321725
median450000
Q3645000
95-th percentile1156600
Maximum7700000
Range7625000
Interquartile range (IQR)323275

Descriptive statistics

Standard deviation367143.05
Coefficient of variation (CV)0.6797875
Kurtosis34.582664
Mean540085.03
Median Absolute Deviation (MAD)150000
Skewness4.0239442
Sum1.1671778 × 1010
Variance1.3479402 × 1011
MonotonicityNot monotonic
2023-01-30T21:58:35.434064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (4018) 20111
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708297
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:35.648176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9300916
Coefficient of variation (CV)0.27592364
Kurtosis49.061898
Mean3.3708297
Median Absolute Deviation (MAD)1
Skewness1.9743199
Sum72847
Variance0.86507039
MonotonicityNot monotonic
2023-01-30T21:58:35.808421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9823
45.5%
4 6881
31.8%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2760
 
12.8%
3 9823
45.5%
4 6881
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6881
31.8%
3 9823
45.5%

bathrooms
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147911
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:36.004846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.770157
Coefficient of variation (CV)0.3641764
Kurtosis1.2801397
Mean2.1147911
Median Absolute Deviation (MAD)0.5
Skewness0.51117018
Sum45702.75
Variance0.59314181
MonotonicityNot monotonic
2023-01-30T21:58:36.208169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5379
24.9%
1 3851
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1446
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (20) 652
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 72
 
0.3%
1 3851
17.8%
1.25 9
 
< 0.1%
1.5 1446
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5379
24.9%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8535
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:36.422043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11426
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1124

Descriptive statistics

Standard deviation918.42241
Coefficient of variation (CV)0.44158035
Kurtosis5.2445011
Mean2079.8535
Median Absolute Deviation (MAD)540
Skewness1.4717446
Sum44947713
Variance843499.72
MonotonicityNot monotonic
2023-01-30T21:58:36.668651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1010 129
 
0.6%
1660 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1560 124
 
0.6%
Other values (1028) 20316
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15107.713
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:36.912701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688.5
95-th percentile43341
Maximum1651359
Range1650839
Interquartile range (IQR)5648.5

Descriptive statistics

Standard deviation41422.347
Coefficient of variation (CV)2.7418012
Kurtosis285.05248
Mean15107.713
Median Absolute Deviation (MAD)2618
Skewness13.059438
Sum3.2649279 × 108
Variance1.7158108 × 109
MonotonicityNot monotonic
2023-01-30T21:58:37.140950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19816
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4943316
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:37.339719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54000341
Coefficient of variation (CV)0.36136786
Kurtosis-0.48489632
Mean1.4943316
Median Absolute Deviation (MAD)0.5
Skewness0.61609393
Sum32294
Variance0.29160368
MonotonicityNot monotonic
2023-01-30T21:58:37.498883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10679
49.4%
2 8241
38.1%
1.5 1909
 
8.8%
3 613
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10679
49.4%
1.5 1909
 
8.8%
2 8241
38.1%
2.5 161
 
0.7%
3 613
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 613
 
2.8%
2.5 161
 
0.7%
2 8241
38.1%
1.5 1909
 
8.8%
1 10679
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
21448 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21611
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

Length

2023-01-30T21:58:37.660753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-30T21:58:37.901552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21611
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21611
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21448
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
19487 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21611
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Length

2023-01-30T21:58:38.230582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-30T21:58:38.424051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21611
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21611
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19487
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
3
14030 
4
5678 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21611
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Length

2023-01-30T21:58:38.583173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-30T21:58:38.770100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21611
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21611
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14030
64.9%
4 5678
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

grade
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568877
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:38.928787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1755023
Coefficient of variation (CV)0.15352221
Kurtosis1.1906555
Mean7.6568877
Median Absolute Deviation (MAD)1
Skewness0.77105867
Sum165473
Variance1.3818058
MonotonicityNot monotonic
2023-01-30T21:58:39.088524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8980
41.6%
8 6067
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.2%
11 399
 
1.8%
5 242
 
1.1%
12 90
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8980
41.6%
8 6067
28.1%
9 2615
 
12.1%
10 1134
 
5.2%
11 399
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 90
 
0.4%
11 399
 
1.8%
10 1134
 
5.2%
9 2615
 
12.1%
8 6067
28.1%
7 8980
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.3961
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:39.290886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.12816
Coefficient of variation (CV)0.46305635
Kurtosis3.4017084
Mean1788.3961
Median Absolute Deviation (MAD)450
Skewness1.4465844
Sum38649028
Variance685796.25
MonotonicityNot monotonic
2023-01-30T21:58:39.515358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (936) 19722
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.45736
Minimum0
Maximum4820
Zeros13125
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:39.739446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.48735
Coefficient of variation (CV)1.518189
Kurtosis2.7166126
Mean291.45736
Median Absolute Deviation (MAD)0
Skewness1.5779206
Sum6298685
Variance195795.06
MonotonicityNot monotonic
2023-01-30T21:58:39.979125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13125
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6900
31.9%
ValueCountFrequency (%)
0 13125
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.008
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:40.208222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.372358
Coefficient of variation (CV)0.014902202
Kurtosis-0.65724347
Mean1971.008
Median Absolute Deviation (MAD)23
Skewness-0.46988344
Sum42595453
Variance862.73543
MonotonicityNot monotonic
2023-01-30T21:58:40.447667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 422
 
2.0%
1977 417
 
1.9%
2007 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17324
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.410069
Minimum0
Maximum2015
Zeros20697
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:40.693203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.69701
Coefficient of variation (CV)4.7588755
Kurtosis18.698968
Mean84.410069
Median Absolute Deviation (MAD)0
Skewness4.5492533
Sum1824186
Variance161360.49
MonotonicityNot monotonic
2023-01-30T21:58:40.925392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20697
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 25
 
0.1%
2006 24
 
0.1%
Other values (60) 570
 
2.6%
ValueCountFrequency (%)
0 20697
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

zipcode
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.942
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:41.173692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.505054
Coefficient of variation (CV)0.00054553606
Kurtosis-0.85349434
Mean98077.942
Median Absolute Deviation (MAD)42
Skewness0.40568218
Sum2.1195624 × 109
Variance2862.7908
MonotonicityNot monotonic
2023-01-30T21:58:41.410194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 601
 
2.8%
98038 590
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 548
 
2.5%
98034 545
 
2.5%
98118 508
 
2.4%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16112
74.6%
ValueCountFrequency (%)
98001 362
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 140
 
0.6%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560046
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:41.669580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.47095
median47.5718
Q347.678
95-th percentile47.74965
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.20705

Descriptive statistics

Standard deviation0.13856801
Coefficient of variation (CV)0.0029135383
Kurtosis-0.67649634
Mean47.560046
Median Absolute Deviation (MAD)0.1049
Skewness-0.48514566
Sum1027820.1
Variance0.019201095
MonotonicityNot monotonic
2023-01-30T21:58:41.894822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5322 17
 
0.1%
47.6846 17
 
0.1%
47.6624 17
 
0.1%
47.5491 17
 
0.1%
47.6711 16
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.5402 15
 
0.1%
47.686 15
 
0.1%
47.6904 15
 
0.1%
Other values (5024) 21450
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21389
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21611
Negative (%)100.0%
Memory size337.7 KiB
2023-01-30T21:58:42.180092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14083134
Coefficient of variation (CV)-0.0011523349
Kurtosis1.0494255
Mean-122.21389
Median Absolute Deviation (MAD)0.101
Skewness0.88504925
Sum-2641164.5
Variance0.019833467
MonotonicityNot monotonic
2023-01-30T21:58:42.581275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 116
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.372 99
 
0.5%
-122.363 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20599
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5596
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:42.818247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.41655
Coefficient of variation (CV)0.34502693
Kurtosis1.5967935
Mean1986.5596
Median Absolute Deviation (MAD)410
Skewness1.1081365
Sum42931539
Variance469795.84
MonotonicityNot monotonic
2023-01-30T21:58:43.032578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 181
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1800 166
 
0.8%
1720 166
 
0.8%
1620 165
 
0.8%
1610 165
 
0.8%
Other values (767) 19848
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12769.025
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-01-30T21:58:43.277629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999
Q15100
median7620
Q310083.5
95-th percentile37064
Maximum871200
Range870549
Interquartile range (IQR)4983.5

Descriptive statistics

Standard deviation27305.37
Coefficient of variation (CV)2.1384067
Kurtosis150.74974
Mean12769.025
Median Absolute Deviation (MAD)2505
Skewness9.5063189
Sum2.7595141 × 108
Variance7.4558322 × 108
MonotonicityNot monotonic
2023-01-30T21:58:43.498968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 357
 
1.7%
6000 289
 
1.3%
7200 211
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
4500 111
 
0.5%
3600 111
 
0.5%
5100 109
 
0.5%
Other values (8679) 19593
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2023-01-30T21:58:29.364325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:20.158856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:23.861252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:29.468477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:34.584797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:39.101291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:43.971510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:48.742907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:52.754252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:56.914852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:01.017897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:04.740364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:08.996782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:12.848997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:17.379719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:21.242581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-01-30T21:58:07.776631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:11.778328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:16.052560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:19.925268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:24.746457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:28.357604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:31.927194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:22.762258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:28.150441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:33.793776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:37.762492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:42.993417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:47.697340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:51.861774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:56.016833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:59.984214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:03.886554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:07.983388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:12.003162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:16.317912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:20.316850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:24.954938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:28.569277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:32.129735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:23.096045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:28.524711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:33.989387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:38.058517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:43.299691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:48.044296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:52.107228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:56.279200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:00.219935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:04.116484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:08.238815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:12.199109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:16.588187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:20.520559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:25.164698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:28.777799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:32.327002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:23.423032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:28.895511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:34.192185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:38.352463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:43.513717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:48.304160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:52.350619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:56.492950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:00.419997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:04.365742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:08.508209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:12.401143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:16.863651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:20.730876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:25.362081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:28.976587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:32.517050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:23.669985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:29.190064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:34.397477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:38.620488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:43.756181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:48.523833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:52.563532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:57:56.711062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:00.802156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:04.565120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:08.741141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:12.623832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:17.129387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:21.007909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:25.561913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-30T21:58:29.175005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-30T21:58:43.731699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorsgradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15waterfrontviewcondition
id1.0000.0040.0060.0150.002-0.1170.0190.0200.0040.0020.027-0.017-0.005-0.0040.0070.000-0.1150.0060.0290.030
price0.0041.0000.3450.4970.6440.0750.3220.6580.5420.2520.1020.102-0.0090.4560.0640.5720.0630.3200.2080.023
bedrooms0.0060.3451.0000.5220.6470.2170.2280.3810.5400.2310.1800.017-0.167-0.0210.1910.4440.2020.0000.0380.024
bathrooms0.0150.4970.5221.0000.7460.0690.5470.6580.6910.1920.5670.043-0.2050.0080.2610.5700.0630.1020.1140.130
sqft_living0.0020.6440.6470.7461.0000.3040.4010.7160.8440.3280.3520.053-0.2070.0310.2850.7470.2840.1400.1490.060
sqft_lot-0.1170.0750.2170.0690.3041.000-0.2340.1520.2720.037-0.0380.009-0.319-0.1220.3710.3600.9220.0140.0400.039
floors0.0190.3220.2280.5470.401-0.2341.0000.5020.599-0.2720.5520.013-0.0620.0250.1490.305-0.2310.0220.0240.179
grade0.0200.6580.3810.6580.7160.1520.5021.0000.7120.0930.5010.016-0.1820.1040.2230.6630.1560.1180.1430.154
sqft_above0.0040.5420.5400.6910.8440.2720.5990.7121.000-0.1660.4720.031-0.279-0.0260.3850.6970.2540.0830.0890.107
sqft_basement0.0020.2520.2310.1920.3280.037-0.2720.093-0.1661.000-0.1780.0630.1150.116-0.2000.1300.0300.1350.1590.094
yr_built0.0270.1020.1800.5670.352-0.0380.5520.5010.472-0.1781.000-0.215-0.317-0.1260.4130.336-0.0160.0320.0410.248
yr_renovated-0.0170.1020.0170.0430.0530.0090.0130.0160.0310.063-0.2151.0000.0620.025-0.075-0.0060.0090.0920.1090.067
zipcode-0.005-0.009-0.167-0.205-0.207-0.319-0.062-0.182-0.2790.115-0.3170.0621.0000.250-0.577-0.287-0.3260.0790.0740.074
lat-0.0040.456-0.0210.0080.031-0.1220.0250.104-0.0260.116-0.1260.0250.2501.000-0.1430.028-0.1170.0340.0680.058
long0.0070.0640.1910.2610.2850.3710.1490.2230.385-0.2000.413-0.075-0.577-0.1431.0000.3800.3730.0960.0850.081
sqft_living150.0000.5720.4440.5700.7470.3600.3050.6630.6970.1300.336-0.006-0.2870.0280.3801.0000.3660.0890.1470.062
sqft_lot15-0.1150.0630.2020.0630.2840.922-0.2310.1560.2540.030-0.0160.009-0.326-0.1170.3730.3661.0000.0000.0350.013
waterfront0.0060.3200.0000.1020.1400.0140.0220.1180.0830.1350.0320.0920.0790.0340.0960.0890.0001.0000.5920.017
view0.0290.2080.0380.1140.1490.0400.0240.1430.0890.1590.0410.1090.0740.0680.0850.1470.0350.5921.0000.025
condition0.0300.0230.0240.1300.0600.0390.1790.1540.1070.0940.2480.0670.0740.0580.0810.0620.0130.0170.0251.000

Missing values

2023-01-30T21:58:32.977015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-30T21:58:33.534500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
071293005202014-10-13221900.031.00118056501.000371180.00195509817847.5112-122.25713405650
164141001922014-12-09538000.032.25257072422.000372170.0400195119919812547.7210-122.31916907639
256315004002015-02-25180000.021.00770100001.00036770.00193309802847.7379-122.23327208062
324872008752014-12-09604000.043.00196050001.000571050.0910196509813647.5208-122.39313605000
419544005102015-02-18510000.032.00168080801.000381680.00198709807447.6168-122.04518007503
572375503102014-05-121225000.044.5054201019301.0003113890.01530200109805347.6561-122.0054760101930
613214000602014-06-27257500.032.25171568192.000371715.00199509800347.3097-122.32722386819
720080002702015-01-15291850.031.50106097111.000371060.00196309819847.4095-122.31516509711
824146001262015-04-15229500.031.00178074701.000371050.0730196009814647.5123-122.33717808113
937935001602015-03-12323000.032.50189065602.000371890.00200309803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2160378521400402014-08-25507250.032.50227055362.000382270.00200309806547.5389-121.88122705731
2160498342013672015-01-26429000.032.00149011263.000381490.00201409814447.5699-122.28814001230
2160534489002102014-10-14610685.042.50252060232.000392520.00201409805647.5137-122.16725206023
2160679360004292015-03-261007500.043.50351072002.000392600.0910200909813647.5537-122.39820506200
2160729978000212015-02-19475000.032.50131012942.000381180.0130200809811647.5773-122.40913301265
216082630000182014-05-21360000.032.50153011313.000381530.00200909810347.6993-122.34615301509
2160966000601202015-02-23400000.042.50231058132.000382310.00201409814647.5107-122.36218307200
2161015233001412014-06-23402101.020.75102013502.000371020.00200909814447.5944-122.29910202007
216112913101002015-01-16400000.032.50160023882.000381600.00200409802747.5345-122.06914101287
2161215233001572014-10-15325000.020.75102010762.000371020.00200809814447.5941-122.29910201357